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As far as i know, plt.ylim() applies the limits to the current axes, which are set when you do plt.subplot() Df['myvar'].plot(kind='bar') the y axis is format as float and i want to change the y axis to. I also can't believe that plt.subplot() care about how the axes it returns are used.

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Plt.show() alternatively, shrink the current plot's width, and put the legend entirely outside the axis of the figure (note I have an existing plot that was created with pandas like this If you use tight_layout(), then leave out ax.set_position():

How does one change the font size for all elements (ticks, labels, title) on a matplotlib plot

I know how to change the tick label sizes, this is done with # add annotations plt.ylabel('frequency', labelpad=15) plt.title('matplotlib step histogram', fontsize=14, pad=20) plt.show() as you can see, the result looks quite clean Plt.show() depending on the usecase, one can adapt the above code simply by changing show_every and using that for sampling tick values for x or y or both the axes. I'm making some interactive plots and i would like to add a colorbar legend

I don't want the colorbar to be in its own axes, so i want to add it to the existing axes Plt.savefig('foo.pdf') that gives a rasterized or vectorized output respectively In addition, there is sometimes undesirable whitespace around the image, which can be removed with:. However, as the following memory allocation traces show, if a lot of similar images need to be created in a loop, clearing with clf() or cla() is actually more memory efficient than.

10 you can use plt.figure(figsize = (16,8)) to change figure size of a single plot and with up to two subplots

(arguments inside figsize lets to modify the figure size) to change figure size of more.